_
As a highly motivated and driven professional, I specialize in the integration of cutting-edge technologies such as Machine Learning, Network Security, Artificial Intelligence, and Software Development. My expertise lies in developing efficient and scalable solutions that optimize the deployment of AI-powered applications. Proficient in Python, C++, and Java, I leverage these languages to deliver impactful solutions with a keen eye for detail. Passionate about solving complex problems through technology, I am committed to staying ahead of industry trends and constantly seeking new challenges to enhance my skills as a software professional.
Programming Languages
Machine Learning
Databases
Frameworks
_
Specializing in Software Development, Artificial Intelligence and Machine Learning, focusing on advanced neural networks and deep learning techniques.
_
- ▸Enhanced system scalability by performing database sharding to distribute data across clusters, achieving a 95% increase in data synchronization efficiency and enabling seamless handling of 1 million+ daily transactions.
- ▸Revamped User and Entity Behavior Analytics (UEBA) using Machine learning pipelines in Python, achieving a 75% boost in anomaly detection accuracy and a 25% drop in false positives for real-time monitoring.
- ▸Architected innovative network solutions utilizing Component-Based Design and Efficient Memory Allocation, optimizing Heap usage and improving overall product efficiency by 98%.
- ▸Developed an ETL pipeline for multi-cloud storage providers (AWS, GCP, Azure) to preprocess, clean, and standardize datasets for global data centers, reducing latency by 30%.
- ▸Designed and implemented Quick-Data-Synchronization using Java, Python, React, NodeJS, and GraphQL, achieving 85% productivity increase and 25% reduction in time-to-market for global systems data sync.
- ▸Integrated multi-cloud storage providers (AWS, GCP, Azure) with OpenText CMS using Python, AngularJS, SQL, and JavaScript, reducing server load by 75% and increasing user retention by 60%
_
_
First-authored and presented at IEEE CCECE 2024, implementing Deep Learning solution that achieved 90% accuracy in real-time analysis, and handled 1,000+ concurrent DICOM analyses with 99.9% uptime.
Published in IEEE Journal, developed a Python based real-time analysis framework that improved scanner efficiency by 75%, and processed over 5,000 imaging records through automated Python and TensorFlow pipeline
Authored in IJMSCR Journal, designing an AI-driven platform achieving 96% diagnostic accuracy, and reducing analysis time by 85% thus improving patient throughput by 40% through automated image classification.